Summary of Dialectical Alignment: Resolving the Tension Of 3h and Security Threats Of Llms, by Shu Yang et al.
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
by Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li, Keyuan Cheng, Muhammad Asif Ali, Lijie Hu, Di Wang
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Dialectical Alignment (DA) framework is a novel approach to ensuring large language models (LLMs) are helpful, honest, and harmless. Existing alignment methods like RLHF and DPO fine-tune LLMs to match preferences in the preference dataset but often lead them to adapt to external evidence, even when it’s poisoned. This can result in LLMs being “Adaptive Chameleons” that change their behavior based on conflicting information. To address this challenge, DA utilizes AI feedback to identify optimal strategies for navigating inter-context conflicts and context-memory conflicts with different external evidence. The framework constructs the SFT and preference datasets based on this feedback and uses them for LLM alignment to defend against poisoned context attacks while preserving in-context knowledge editing effectiveness. Experimental results show that DA improves poisoned data attack defense by 20% without requiring additional prompt engineering or prior declaration of “you may be attacked” to the LLM’s context window. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are important tools, but they need to be “helpful, honest, and harmless.” Some people worry that these models might change what they say based on external information that is false. Researchers have been working on ways to make sure these models behave well, even when given bad information. A new approach called Dialectical Alignment (DA) tries to solve this problem by using artificial intelligence feedback to help the model decide how to respond when it gets conflicting information. The DA framework builds special datasets that help the model learn how to defend against false information and still provide useful results. So far, experiments show that DA can improve defenses against attacks by 20% without needing any extra work or warnings. |
Keywords
» Artificial intelligence » Alignment » Context window » Prompt » Rlhf
